def train_rnn_ae(args):
# General
trajectories_path = args.trajectories # e.g. .../11
camera_id = os.path.basename(trajectories_path)
video_resolution = [
float(measurement) for measurement in args.video_resolution.split('x')
]
video_resolution = np.array(video_resolution, dtype=np.float32)
# Architecture
model_type = args.model_type
extract_delta = args.extract_delta
use_first_step_as_reference = args.use_first_step_as_reference
input_length = args.input_length
input_gap = args.input_gap
rec_length = args.rec_length
pred_length = args.pred_length
hidden_dims = args.hidden_dims
output_activation = args.output_activation
cell_type = args.cell_type
disable_reconstruction_branch = args.disable_reconstruction_branch
reconstruct_reverse = args.reconstruct_reverse
conditional_reconstruction = args.conditional_reconstruction
conditional_prediction = args.conditional_prediction
# Training
optimiser = args.optimiser
learning_rate = args.learning_rate
loss = args.loss
l1_reg = args.l1_reg
l2_reg = args.l2_reg
epochs = args.epochs
batch_size = args.batch_size
input_missing_steps = args.input_missing_steps
coordinate_system = args.coordinate_system
normalisation_strategy = args.normalisation_strategy
# Logging
root_log_dir = args.root_log_dir
resume_training = args.resume_training
trajectories = load_trajectories(trajectories_path)
print('\nLoaded %d trajectories.' % len(trajectories))
trajectories = remove_short_trajectories(trajectories,
input_length=input_length,
input_gap=input_gap,
pred_length=pred_length)
print('\nRemoved short trajectories. Number of trajectories left: %d.' %
len(trajectories))
X_train, y_train, X_val, y_val, scaler = \
produce_data_from_model_type(model_type, trajectories, video_resolution=video_resolution,
input_length=input_length, input_gap=input_gap, pred_length=pred_length,
normalisation_strategy=normalisation_strategy, coordinate_system=coordinate_system,
input_missing_steps=input_missing_steps, extract_delta=extract_delta,
use_first_step_as_reference=use_first_step_as_reference)
if model_type == 'concatenate':
loss = 'balanced_' + loss
print('\nInstantiating anomaly model ...')
input_dim = X_train.shape[-1]
rnn_ae = RNNEncoderDecoder(
input_length=input_length,
input_dim=input_dim,
reconstruction_length=rec_length,
prediction_length=pred_length,
hidden_dims=hidden_dims,
output_activation=output_activation,
cell_type=cell_type,
reconstruction_branch=disable_reconstruction_branch,
reconstruct_reverse=reconstruct_reverse,
conditional_reconstruction=conditional_reconstruction,
conditional_prediction=conditional_prediction,
optimiser=optimiser,
learning_rate=learning_rate,
loss=loss,
l1_reg=l1_reg,
l2_reg=l2_reg)
log_dir = set_up_logging(camera_id=camera_id,
root_log_dir=root_log_dir,
resume_training=resume_training)
last_epoch = resume_training_from_last_epoch(
model=rnn_ae, resume_training=resume_training)
rnn_ae.train(X_train,
y_train,
epochs=epochs,
initial_epoch=last_epoch,
batch_size=batch_size,
val_data=(X_val, y_val),
log_dir=log_dir)
print('\nRNN Autoencoder anomaly model successfully trained.')
if log_dir is not None:
file_name = os.path.join(log_dir, 'scaler.pkl')
joblib.dump(scaler, filename=file_name)
print('log files were written to: %s' % log_dir)
return rnn_ae, scaler
def train_ae(args):
# General
trajectories_path = args.trajectories # e.g. .../03
camera_id = os.path.basename(trajectories_path)
video_resolution = [
float(measurement) for measurement in args.video_resolution.split('x')
]
video_resolution = np.array(video_resolution, dtype=np.float32)
# Architecture
global_model = args.global_model
hidden_dims = args.hidden_dims
output_activation = args.output_activation
coordinate_system = args.coordinate_system
normalisation_strategy = args.normalisation_strategy
# Training
optimiser = args.optimiser
learning_rate = args.learning_rate
loss = args.loss
epochs = args.epochs
batch_size = args.batch_size
# Logging
root_log_dir = args.root_log_dir
resume_training = args.resume_training
trajectories = load_trajectories(trajectories_path)
print('\nLoaded %d trajectories.' % len(trajectories))
if global_model:
trajectories = extract_global_features(
trajectories, video_resolution=video_resolution)
coordinate_system = 'global'
print(
'\nExtracted global features from input skeletons. In addition, the coordinate system has been set '
'to global.')
trajectories = change_coordinate_system(
trajectories,
video_resolution=video_resolution,
coordinate_system=coordinate_system,
invert=False)
print('\nChanged coordinate system to %s.' % coordinate_system)
trajectories_train, trajectories_val = split_into_train_and_test(
trajectories, train_ratio=0.8, seed=42)
X_train = shuffle(aggregate_autoencoder_data(trajectories_train),
random_state=42)
X_val = aggregate_autoencoder_data(trajectories_val)
X_train, scaler = scale_trajectories(X_train,
strategy=normalisation_strategy)
X_val, _ = scale_trajectories(X_val,
scaler=scaler,
strategy=normalisation_strategy)
print('\nNormalised input features using the %s normalisation strategy.' %
normalisation_strategy)
input_dim = X_train.shape[-1]
ae = Autoencoder(input_dim=input_dim,
hidden_dims=hidden_dims,
output_activation=output_activation,
optimiser=optimiser,
learning_rate=learning_rate,
loss=loss)
log_dir = set_up_logging(camera_id=camera_id,
root_log_dir=root_log_dir,
resume_training=resume_training)
last_epoch = resume_training_from_last_epoch(
model=ae, resume_training=resume_training)
ae.train(X_train,
X_train,
epochs=epochs,
initial_epoch=last_epoch,
batch_size=batch_size,
val_data=(X_val, X_val),
log_dir=log_dir)
print('Autoencoder anomaly model successfully trained.')
if log_dir is not None:
file_name = os.path.join(log_dir, 'scaler.pkl')
joblib.dump(scaler, filename=file_name)
print('log files were written to: %s' % log_dir)
return ae, scaler
def train_complete_rnn_ae(args):
# General
trajectories_path = args.trajectories
camera_id = os.path.basename(trajectories_path)
video_resolution = [
float(measurement) for measurement in args.video_resolution.split('x')
]
video_resolution = np.array(video_resolution, dtype=np.float32)
# Architecture
input_length = args.input_length
pred_length = args.pred_length
global_hidden_dims = args.global_hidden_dims
local_hidden_dims = args.local_hidden_dims
extra_hidden_dims = args.extra_hidden_dims
cell_type = args.cell_type
reconstruct_reverse = args.reconstruct_reverse
# Training
optimiser = args.optimiser
learning_rate = args.learning_rate
loss = args.loss
epochs = args.epochs
batch_size = args.batch_size
global_normalisation_strategy = args.global_normalisation_strategy
local_normalisation_strategy = args.local_normalisation_strategy
# Logging
root_log_dir = args.root_log_dir
resume_training = args.resume_training
_, trajectories_coordinates = load_trajectories(trajectories_path)
print('\nLoaded %d trajectories.' % len(trajectories_coordinates))
trajectories_coordinates = remove_short_trajectories(
trajectories_coordinates,
input_length=input_length,
input_gap=0,
pred_length=pred_length)
print('\nRemoved short trajectories. Number of trajectories left: %d.' %
len(trajectories_coordinates))
trajectories_coordinates_train, trajectories_coordinates_val = \
train_test_split_through_time(trajectories_coordinates, input_length=input_length, pred_length=pred_length,
train_ratio=0.8)
# Global pre-processing
global_features_train = extract_global_features(
trajectories_coordinates_train, video_resolution=video_resolution)
global_features_val = extract_global_features(
trajectories_coordinates_val, video_resolution=video_resolution)
global_features_train = change_coordinate_system(
global_features_train,
video_resolution=video_resolution,
coordinate_system='global',
invert=False)
global_features_val = change_coordinate_system(
global_features_val,
video_resolution=video_resolution,
coordinate_system='global',
invert=False)
global_features_train, global_scaler = scale_trajectories(
global_features_train, strategy=global_normalisation_strategy)
global_features_val, _ = scale_trajectories(
global_features_val,
scaler=global_scaler,
strategy=global_normalisation_strategy)
# Local pre-processing
local_features_train = deepcopy(trajectories_coordinates_train)
local_features_val = deepcopy(trajectories_coordinates_val)
local_features_train = change_coordinate_system(
local_features_train,
video_resolution=video_resolution,
coordinate_system='bounding_box_centre',
invert=False)
local_features_val = change_coordinate_system(
local_features_val,
video_resolution=video_resolution,
coordinate_system='bounding_box_centre',
invert=False)
local_features_train, local_scaler = scale_trajectories(
local_features_train, strategy=local_normalisation_strategy)
local_features_val, _ = scale_trajectories(
local_features_val,
scaler=local_scaler,
strategy=local_normalisation_strategy)
# # Output
# out_train = trajectories_coordinates_train
# out_val = trajectories_coordinates_val
#
# out_train = change_coordinate_system(out_train,
# video_resolution=video_resolution,
# coordinate_system='global', invert=False)
# out_val = change_coordinate_system(out_val,
# video_resolution=video_resolution,
# coordinate_system='global', invert=False)
# Anomaly Model
global_input_dim = extract_input_dim(global_features_train)
local_input_dim = extract_input_dim(local_features_train)
anomaly_model = CombinedEncoderDecoder(
input_length=input_length,
global_input_dim=global_input_dim,
local_input_dim=local_input_dim,
prediction_length=pred_length,
global_hidden_dims=global_hidden_dims,
local_hidden_dims=local_hidden_dims,
extra_hidden_dims=extra_hidden_dims,
cell_type=cell_type,
reconstruct_reverse=reconstruct_reverse,
optimiser=optimiser,
learning_rate=learning_rate,
loss=loss)
# Set up training logging (optional)
log_dir = set_up_logging(camera_id=camera_id,
root_log_dir=root_log_dir,
resume_training=resume_training)
# Resume training (optional)
last_epoch = resume_training_from_last_epoch(
model=anomaly_model, resume_training=resume_training)
if pred_length > 0:
# Global
X_global_train, y_global_train = list(
zip(*[
collect_trajectories(global_trajectory, input_length, 0,
pred_length)
for global_trajectory in global_features_train.values()
]))
X_global_train, y_global_train = np.vstack(X_global_train), np.vstack(
y_global_train)
X_global_val, y_global_val = list(
zip(*[
collect_trajectories(global_trajectory, input_length, 0,
pred_length)
for global_trajectory in global_features_val.values()
]))
X_global_val, y_global_val = np.vstack(X_global_val), np.vstack(
y_global_val)
# Local
X_local_train, y_local_train = list(
zip(*[
collect_trajectories(local_trajectory, input_length, 0,
pred_length)
for local_trajectory in local_features_train.values()
]))
X_local_train, y_local_train = np.vstack(X_local_train), np.vstack(
y_local_train)
X_local_val, y_local_val = list(
zip(*[
collect_trajectories(local_trajectory, input_length, 0,
pred_length)
for local_trajectory in local_features_val.values()
]))
X_local_val, y_local_val = np.vstack(X_local_val), np.vstack(
y_local_val)
# # Output
# X_out_train, y_out_train = list(zip(*[collect_trajectories(out_trajectory,
# input_length, 0, pred_length)
# for out_trajectory in out_train.values()]))
# X_out_train, y_out_train = np.vstack(X_out_train), np.vstack(y_out_train)
#
# X_out_val, y_out_val = list(zip(*[collect_trajectories(out_trajectory,
# input_length, 0, pred_length)
# for out_trajectory in out_val.values()]))
# X_out_val, y_out_val = np.vstack(X_out_val), np.vstack(y_out_val)
#
# X_global_train, X_local_train, X_out_train, y_global_train, y_local_train, y_out_train = \
# shuffle(X_global_train, X_local_train, X_out_train, y_global_train, y_local_train, y_out_train,
# random_state=42)
X_global_train, X_local_train, y_global_train, y_local_train = \
shuffle(X_global_train, X_local_train, y_global_train, y_local_train, random_state=42)
# X_train = [X_global_train, X_local_train, X_out_train]
# y_train = [y_global_train, y_local_train, y_out_train]
# val_data = ([X_global_val, X_local_val, X_out_val], [y_global_val, y_local_val, y_out_val])
X_train = [X_global_train, X_local_train]
y_train = [y_global_train, y_local_train]
val_data = ([X_global_val, X_local_val], [y_global_val, y_local_val])
anomaly_model.train(X_train,
y_train,
epochs=epochs,
initial_epoch=last_epoch,
batch_size=batch_size,
val_data=val_data,
log_dir=log_dir)
else:
# Global
X_global_train = [
collect_trajectories(global_trajectory, input_length, 0,
pred_length)
for global_trajectory in global_features_train.values()
]
X_global_train = np.vstack(X_global_train)
X_global_val = [
collect_trajectories(global_trajectory, input_length, 0,
pred_length)
for global_trajectory in global_features_val.values()
]
X_global_val = np.vstack(X_global_val)
# Local
X_local_train = [
collect_trajectories(local_trajectory, input_length, 0,
pred_length)
for local_trajectory in local_features_train.values()
]
X_local_train = np.vstack(X_local_train)
X_local_val = [
collect_trajectories(local_trajectory, input_length, 0,
pred_length)
for local_trajectory in local_features_val.values()
]
X_local_val = np.vstack(X_local_val)
# # Output
# X_out_train = [collect_trajectories(out_trajectory, input_length, 0, pred_length)
# for out_trajectory in out_train.values()]
# X_out_train = np.vstack(X_out_train)
#
# X_out_val = [collect_trajectories(out_trajectory, input_length, 0, pred_length)
# for out_trajectory in out_val.values()]
# X_out_val = np.vstack(X_out_val)
# X_global_train, X_local_train, X_out_train = shuffle(X_global_train, X_local_train, X_out_train,
# random_state=42)
X_global_train, X_local_train = shuffle(X_global_train,
X_local_train,
random_state=42)
# X_train = [X_global_train, X_local_train, X_out_train]
# val_data = ([X_global_val, X_local_val, X_out_val],)
X_train = [X_global_train, X_local_train]
val_data = ([X_global_val, X_local_val], )
anomaly_model.train(X_train,
epochs=epochs,
initial_epoch=last_epoch,
batch_size=batch_size,
val_data=val_data,
log_dir=log_dir)
print('Combined global and local anomaly model successfully trained.')
if log_dir is not None:
joblib.dump(global_scaler,
filename=os.path.join(log_dir, 'global_scaler.pkl'))
joblib.dump(local_scaler,
filename=os.path.join(log_dir, 'local_scaler.pkl'))
print('log files were written to: %s' % log_dir)
return None
def train_combined_model(args):
# General
trajectories_path = args.trajectories
camera_id = os.path.basename(trajectories_path)
video_resolution = [float(measurement) for measurement in args.video_resolution.split('x')]
video_resolution = np.array(video_resolution, dtype=np.float32)
# Architecture
message_passing = args.message_passing
reconstruct_original_data = args.reconstruct_original_data
multiple_outputs = args.multiple_outputs
multiple_outputs_before_concatenation = args.multiple_outputs_before_concatenation
input_length = args.input_length
rec_length = args.rec_length
pred_length = args.pred_length
global_hidden_dims = args.global_hidden_dims
local_hidden_dims = args.local_hidden_dims
extra_hidden_dims = args.extra_hidden_dims
output_activation = args.output_activation
cell_type = args.cell_type
reconstruct_reverse = args.reconstruct_reverse
# Training
optimiser = args.optimiser
learning_rate = args.learning_rate
loss = args.loss
l1_reg = args.l1_reg
l2_reg = args.l2_reg
epochs = args.epochs
batch_size = args.batch_size
input_missing_steps = args.input_missing_steps
global_normalisation_strategy = args.global_normalisation_strategy
local_normalisation_strategy = args.local_normalisation_strategy
out_normalisation_strategy = args.out_normalisation_strategy
# Logging
root_log_dir = args.root_log_dir
resume_training = args.resume_training
trajectories = load_trajectories(trajectories_path)
print('\nLoaded %d trajectories.' % len(trajectories))
trajectories = remove_short_trajectories(trajectories, input_length=input_length,
input_gap=0, pred_length=pred_length)
print('\nRemoved short trajectories. Number of trajectories left: %d.' % len(trajectories))
trajectories_train, trajectories_val = split_into_train_and_test(trajectories, train_ratio=0.8, seed=42)
if input_missing_steps:
trajectories_train = input_trajectories_missing_steps(trajectories_train)
print('\nInputted missing steps of trajectories.')
# TODO: General function to extract features
# X_..._train, X_..._val, y_..._train, y_..._val, ..._scaler = general_function()
# Global
global_trajectories_train = extract_global_features(deepcopy(trajectories_train), video_resolution=video_resolution)
global_trajectories_val = extract_global_features(deepcopy(trajectories_val), video_resolution=video_resolution)
global_trajectories_train = change_coordinate_system(global_trajectories_train, video_resolution=video_resolution,
coordinate_system='global', invert=False)
global_trajectories_val = change_coordinate_system(global_trajectories_val, video_resolution=video_resolution,
coordinate_system='global', invert=False)
print('\nChanged global trajectories\'s coordinate system to global.')
_, global_scaler = scale_trajectories(aggregate_autoencoder_data(global_trajectories_train),
strategy=global_normalisation_strategy)
X_global_train, y_global_train = aggregate_rnn_autoencoder_data(global_trajectories_train,
input_length=input_length,
input_gap=0, pred_length=pred_length)
X_global_val, y_global_val = aggregate_rnn_autoencoder_data(global_trajectories_val, input_length=input_length,
input_gap=0, pred_length=pred_length)
X_global_train, _ = scale_trajectories(X_global_train, scaler=global_scaler, strategy=global_normalisation_strategy)
X_global_val, _ = scale_trajectories(X_global_val, scaler=global_scaler, strategy=global_normalisation_strategy)
if y_global_train is not None and y_global_val is not None:
y_global_train, _ = scale_trajectories(y_global_train, scaler=global_scaler,
strategy=global_normalisation_strategy)
y_global_val, _ = scale_trajectories(y_global_val, scaler=global_scaler, strategy=global_normalisation_strategy)
print('\nNormalised global trajectories using the %s normalisation strategy.' % global_normalisation_strategy)
# Local
local_trajectories_train = deepcopy(trajectories_train) if reconstruct_original_data else trajectories_train
local_trajectories_val = deepcopy(trajectories_val) if reconstruct_original_data else trajectories_val
local_trajectories_train = change_coordinate_system(local_trajectories_train, video_resolution=video_resolution,
coordinate_system='bounding_box_centre', invert=False)
local_trajectories_val = change_coordinate_system(local_trajectories_val, video_resolution=video_resolution,
coordinate_system='bounding_box_centre', invert=False)
print('\nChanged local trajectories\'s coordinate system to bounding_box_centre.')
_, local_scaler = scale_trajectories(aggregate_autoencoder_data(local_trajectories_train),
strategy=local_normalisation_strategy)
X_local_train, y_local_train = aggregate_rnn_autoencoder_data(local_trajectories_train, input_length=input_length,
input_gap=0, pred_length=pred_length)
X_local_val, y_local_val = aggregate_rnn_autoencoder_data(local_trajectories_val, input_length=input_length,
input_gap=0, pred_length=pred_length)
X_local_train, _ = scale_trajectories(X_local_train, scaler=local_scaler, strategy=local_normalisation_strategy)
X_local_val, _ = scale_trajectories(X_local_val, scaler=local_scaler, strategy=local_normalisation_strategy)
if y_local_train is not None and y_local_val is not None:
y_local_train, _ = scale_trajectories(y_local_train, scaler=local_scaler, strategy=local_normalisation_strategy)
y_local_val, _ = scale_trajectories(y_local_val, scaler=local_scaler, strategy=local_normalisation_strategy)
print('\nNormalised local trajectories using the %s normalisation strategy.' % local_normalisation_strategy)
# (Optional) Reconstruct the original data
if reconstruct_original_data:
print('\nReconstruction/Prediction target is the original data.')
out_trajectories_train = trajectories_train
out_trajectories_val = trajectories_val
out_trajectories_train = change_coordinate_system(out_trajectories_train, video_resolution=video_resolution,
coordinate_system='global', invert=False)
out_trajectories_val = change_coordinate_system(out_trajectories_val, video_resolution=video_resolution,
coordinate_system='global', invert=False)
print('\nChanged target trajectories\'s coordinate system to global.')
_, out_scaler = scale_trajectories(aggregate_autoencoder_data(out_trajectories_train),
strategy=out_normalisation_strategy)
X_out_train, y_out_train = aggregate_rnn_autoencoder_data(out_trajectories_train, input_length=input_length,
input_gap=0, pred_length=pred_length)
X_out_val, y_out_val = aggregate_rnn_autoencoder_data(out_trajectories_val, input_length=input_length,
input_gap=0, pred_length=pred_length)
X_out_train, _ = scale_trajectories(X_out_train, scaler=out_scaler, strategy=out_normalisation_strategy)
X_out_val, _ = scale_trajectories(X_out_val, scaler=out_scaler, strategy=out_normalisation_strategy)
if y_out_train is not None and y_out_val is not None:
y_out_train, _ = scale_trajectories(y_out_train, scaler=out_scaler, strategy=out_normalisation_strategy)
y_out_val, _ = scale_trajectories(y_out_val, scaler=out_scaler, strategy=out_normalisation_strategy)
print('\nNormalised target trajectories using the %s normalisation strategy.' % out_normalisation_strategy)
# Shuffle training data and assemble training and validation sets
if y_global_train is not None:
if reconstruct_original_data:
X_global_train, X_local_train, X_out_train, y_global_train, y_local_train, y_out_train = \
shuffle(X_global_train, X_local_train, X_out_train,
y_global_train, y_local_train, y_out_train, random_state=42)
X_train = [X_global_train, X_local_train, X_out_train]
y_train = [y_global_train, y_local_train, y_out_train]
val_data = ([X_global_val, X_local_val, X_out_val], [y_global_val, y_local_val, y_out_val])
else:
X_global_train, X_local_train, y_global_train, y_local_train = \
shuffle(X_global_train, X_local_train, y_global_train, y_local_train, random_state=42)
X_train = [X_global_train, X_local_train]
y_train = [y_global_train, y_local_train]
val_data = ([X_global_val, X_local_val], [y_global_val, y_local_val])
else:
if reconstruct_original_data:
X_global_train, X_local_train, X_out_train = \
shuffle(X_global_train, X_local_train, X_out_train, random_state=42)
X_train = [X_global_train, X_local_train, X_out_train]
y_train = None
val_data = ([X_global_val, X_local_val, X_out_val],)
else:
X_global_train, X_local_train = shuffle(X_global_train, X_local_train, random_state=42)
X_train = [X_global_train, X_local_train]
y_train = None
val_data = ([X_global_val, X_local_val],)
# Model
print('\nInstantiating combined anomaly model ...')
global_input_dim = X_global_train.shape[-1]
local_input_dim = X_local_train.shape[-1]
model_args = {'input_length': input_length, 'global_input_dim': global_input_dim,
'local_input_dim': local_input_dim, 'reconstruction_length': rec_length,
'prediction_length': pred_length, 'global_hidden_dims': global_hidden_dims,
'local_hidden_dims': local_hidden_dims, 'extra_hidden_dims': extra_hidden_dims,
'output_activation': output_activation, 'cell_type': cell_type,
'reconstruct_reverse': reconstruct_reverse, 'reconstruct_original_data': reconstruct_original_data,
'multiple_outputs': multiple_outputs,
'multiple_outputs_before_concatenation': multiple_outputs_before_concatenation,
'optimiser': optimiser, 'learning_rate': learning_rate, 'loss': loss,
'l1_reg': l1_reg, 'l2_reg': l2_reg}
if message_passing:
combined_rnn_ae = MessagePassingEncoderDecoder(**model_args)
else:
combined_rnn_ae = CombinedEncoderDecoder(**model_args)
log_dir = set_up_logging(camera_id=camera_id, root_log_dir=root_log_dir, resume_training=resume_training)
last_epoch = resume_training_from_last_epoch(model=combined_rnn_ae, resume_training=resume_training)
combined_rnn_ae.train(X_train, y_train, epochs=epochs, initial_epoch=last_epoch, batch_size=batch_size,
val_data=val_data, log_dir=log_dir)
print('\nCombined anomaly model successfully trained.')
if log_dir is not None:
global_scaler_file_name = os.path.join(log_dir, 'global_scaler.pkl')
local_scaler_file_name = os.path.join(log_dir, 'local_scaler.pkl')
joblib.dump(global_scaler, filename=global_scaler_file_name)
joblib.dump(local_scaler, filename=local_scaler_file_name)
if reconstruct_original_data:
out_scaler_file_name = os.path.join(log_dir, 'out_scaler.pkl')
joblib.dump(out_scaler, filename=out_scaler_file_name)
print('log files were written to: %s' % log_dir)
if reconstruct_original_data:
return combined_rnn_ae, global_scaler, local_scaler, out_scaler
return combined_rnn_ae, global_scaler, local_scaler
def train_rnn_ae(args):
# General
trajectories_path = args.trajectories # e.g. .../11
camera_id = os.path.basename(args.trajectories_path)
video_resolution = [
float(measurement) for measurement in args.video_resolution.split('x')
]
video_resolution = np.array(video_resolution, dtype=np.float32)
# Architecture
global_model = args.global_model
input_length = args.input_length
input_gap = args.input_gap
pred_length = args.pred_length
hidden_dims = args.hidden_dims
cell_type = args.cell_type
disable_reconstruction_branch = args.disable_reconstruction_branch
reconstruct_reverse = args.reconstruct_reverse
conditional_reconstruction = args.conditional_reconstruction
conditional_prediction = args.conditional_prediction
# Training
optimiser = args.optimiser
learning_rate = args.learning_rate
loss = args.loss
epochs = args.epochs
batch_size = args.batch_size
input_missing_steps = args.input_missing_steps
coordinate_system = args.coordinate_system
normalisation_strategy = args.normalisation_strategy
# Logging
root_log_dir = args.root_log_dir
resume_training = args.resume_training
# trajectories_coordinates is a dictionary where the keys uniquely identify each person in each video and the values
# are float32 tensors. Each tensor represents the detected trajectory of a single person and has shape
# (trajectory_length, input_dim). trajectory_length is the total number of frames for which the person was tracked
# and each detection is composed of k key points (17 for now), each represented by a pair of (x, y) coordinates.
_, trajectories_coordinates = load_trajectories(trajectories_path)
print('\nLoaded %d trajectories.' % len(trajectories_coordinates))
# Filter-out short trajectories
trajectories_coordinates = remove_short_trajectories(
trajectories_coordinates,
input_length=input_length,
input_gap=input_gap,
pred_length=pred_length)
print('\nRemoved short trajectories. Number of trajectories left: %d.' %
len(trajectories_coordinates))
# Global model (optional)
if global_model:
trajectories_coordinates = extract_global_features(
trajectories_coordinates, video_resolution=video_resolution)
coordinate_system = 'global'
print(
'\nExtracted global features from input trajectories. In addition, the coordinate system has been set '
'to global.')
# Change coordinate system
trajectories_coordinates = change_coordinate_system(
trajectories_coordinates,
video_resolution=video_resolution,
coordinate_system=coordinate_system,
invert=False)
print('\nChanged coordinate system to %s.' % coordinate_system)
# Split into training and validation sets
trajectories_coordinates_train, trajectories_coordinates_val = \
train_test_split_through_time(trajectories_coordinates, input_length=input_length, pred_length=pred_length,
train_ratio=0.8)
# Input missing steps (optional)
if input_missing_steps:
trajectories_coordinates_train = input_trajectories_missing_steps(
trajectories_coordinates_train)
# Normalise the data
trajectories_coordinates_train, scaler = scale_trajectories(
trajectories_coordinates_train, strategy=normalisation_strategy)
trajectories_coordinates_val, _ = scale_trajectories(
trajectories_coordinates_val,
scaler=scaler,
strategy=normalisation_strategy)
print('\nInput features normalised using the %s normalisation strategy.' %
normalisation_strategy)
print('\nInstantiating anomaly model ...')
input_dim = extract_input_dim(trajectories_coordinates)
anomaly_model = RNNEncoderDecoder(
input_length=input_length,
input_dim=input_dim,
prediction_length=pred_length,
hidden_dims=hidden_dims,
cell_type=cell_type,
reconstruction_branch=disable_reconstruction_branch,
reconstruct_reverse=reconstruct_reverse,
conditional_reconstruction=conditional_reconstruction,
conditional_prediction=conditional_prediction,
optimiser=optimiser,
learning_rate=learning_rate,
loss=loss)
# Set up training logging (optional)
log_dir = set_up_logging(camera_id=camera_id,
root_log_dir=root_log_dir,
resume_training=resume_training)
# Resume training (optional)
last_epoch = resume_training_from_last_epoch(
model=anomaly_model, resume_training=resume_training)
# Train the anomaly model
if pred_length > 0:
X_train, y_train = list(
zip(*[
collect_trajectories(trajectory_coordinates, input_length,
input_gap, pred_length)
for trajectory_coordinates in
trajectories_coordinates_train.values()
]))
X_train, y_train = np.vstack(X_train), np.vstack(y_train)
print('\nTrain trajectories\' shape: (%d, %d, %d).' %
(X_train.shape[0], X_train.shape[1], X_train.shape[2]))
print('Train future trajectories\' shape: (%d, %d, %d).' %
(y_train.shape[0], y_train.shape[1], y_train.shape[2]))
X_val, y_val = list(
zip(*[
collect_trajectories(trajectory_coordinates, input_length,
input_gap, pred_length)
for trajectory_coordinates in
trajectories_coordinates_val.values()
]))
X_val, y_val = np.vstack(X_val), np.vstack(y_val)
print('\nVal trajectories\' shape: (%d, %d, %d).' %
(X_val.shape[0], X_val.shape[1], X_val.shape[2]))
print('Val future trajectories\' shape: (%d, %d, %d).' %
(y_val.shape[0], y_val.shape[1], y_val.shape[2]))
X_train, y_train = shuffle(X_train, y_train, random_state=42)
anomaly_model.train(X_train,
y_train,
epochs=epochs,
initial_epoch=last_epoch,
batch_size=batch_size,
val_data=(X_val, y_val),
log_dir=log_dir)
else:
X_train = [
collect_trajectories(trajectory_coordinates, input_length,
input_gap, pred_length) for
trajectory_coordinates in trajectories_coordinates_train.values()
]
X_train = np.vstack(X_train)
print('\nTrain trajectories\' shape: (%d, %d, %d).' %
(X_train.shape[0], X_train.shape[1], X_train.shape[2]))
X_val = [
collect_trajectories(trajectory_coordinates, input_length,
input_gap, pred_length) for
trajectory_coordinates in trajectories_coordinates_val.values()
]
X_val = np.vstack(X_val)
print('\nVal trajectories\' shape: (%d, %d, %d).' %
(X_val.shape[0], X_val.shape[1], X_val.shape[2]))
X_train = shuffle(X_train, random_state=42)
anomaly_model.train(X_train,
epochs=epochs,
initial_epoch=last_epoch,
batch_size=batch_size,
val_data=(X_val, ),
log_dir=log_dir)
print('\nRNN Autoencoder model successfully trained.')
if log_dir is not None:
joblib.dump(scaler, filename=os.path.join(log_dir, 'scaler.pkl'))
print('log files were written to: %s' % log_dir)
return None